Towards an artificial intelligence model for the automated prediction of building value

Abstract

Property markets are volatile, necessitating the constant recalculation of property values for a variety of reasons including for the efficient design and construction of buildings. This research is aimed at the automation of the property valuation process using Artificial Intelligence. In a three-part research effort, a Multiple Criteria Decision Method (MCDM) approach using Complex Proportional Assessment (COPRAS) is first applied to predict the property value of new residential units on the basis of a comprehensive list of building characteristic variables identified as relevant for describing a particular property type (in the test case, terraced houses). For initial testing and validation of the valuation prediction calculations, the weights and values of criteria are determined through experts’ opinions and the estimated value of a test property is derived. This first part of the research is described in this report. The second phase of the research involves the automatic acquisition of the variables’ values for any building from the recently digitalised Estonian Building Register. The third part of the research focuses on replacing the need for experts’ opinions of the relative importance weightings of variables through the use of an Artificial Neural Network (ANN) model which is to be trained on existing and continuously refined on new property transaction price data and property characteristics from building permit applications and existing building registers. Parts two and three of this research are still to be carried out and they are outlined in this research paper. It is anticipated that this research will lead to greater efficiency and sustainability through better alignment between building design, construction and market-based property values.